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New Methods in Statistical Speech Recognition
Klusáček, David ; Hajič, Jan (advisor) ; Psutka, Josef (referee) ; Černocký, Jan (referee)
Title: New Methods in Statistical Speech Recognition Author: David Klusáček Department: Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics in Prague, Malostranské náměstí 25, 118 00 Praha 1. Advisor: Prof. RNDr. Jan Hajič, Dr., Institute of Formal and Applied Linguistics. Abstract: This works aims to identify limits of contemporary speech rec- ognizers and tries to come up with methods that could push back the fron- tiers. After describing the state of the art, the weakest link of the chain has been identified in the acoustic front-end, especially when working in harsh acoustic conditions. NUFIBA front-end, the proposed solution, includes re- verb compensation and speaker/background segmentation as well as contin- uous SNR monitoring which, thru cooperation with acoustic model, hinders from avalanche spreading of recognition errors. Owing to the lack of time, only a phoneme recognizer was finally implemented, although large blocks of originally intended word-based continuous speech recognizer were implemented and tested (such as the MMI-class based language model).
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Speech Recognition of Czech Using Finite-State Machines
Podveský, Petr ; Hajič, Jan (advisor) ; Psutka, Josef (referee) ; Krbec, Pavel (referee)
Speech recognition has become a thriving field with many real-life applications. Voice dialing in cell phones, voice control in embedded devices, speech-driven interactive manuals and many other utilities rely on solid speech recognition software. We believe that research in speech recognition can boost performance of many applications related to the area. The thesis concentrates on automatic large-vocabulary continuous-speech recognition of Czech. Czech differs from English in a few aspects. We focus on these differences and propose new language-depended techniques. Namely rich morphology is investigated and its impact on speech recognition is studied. Out-of-vocabulary (OOV) words are identified as one of the major sources deteriorating recognition performace. New language modeling techniques are proposed to alleviate the problem of OOV words. The proposed language models are tested in speech recognition systems on diverse speech corpora. The obtained results validate the original approach to language modeling. Significant overall speech recognition improvement is observed.
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Goal Oriented and Open Domain Dialogue Management
Vodolán, Miroslav ; Jurčíček, Filip (advisor) ; Psutka, Josef (referee) ; Šedivý, Jan (referee)
Title: Goal Oriented and Open Domain Dialogue Management Author: Miroslav Vodolán Department: Institute of Formal and Applied Linguistics Supervisor: Ing. Mgr. Filip Jurčíček, Ph.D., Institute of Formal and Applied Linguistics Abstract: This thesis proposes novel approaches for dialogue management in dialogue sys- tems. It covers goal-oriented and open domain dialogue systems. In both setups, it helps to improve quality of dialogues between the system and its users: 1) In the case of goal-oriented dialogues, we improve the accuracy of dialogue state tracking methods of spoken dialogue systems. Our approach limits the effect of automatic speech recognition (ASR) errors. We incrementally enhance our interpretable rule-based core by complex neural networks. The resulting system achieves several published state-of-the-art results on public datasets. 2) Effective dialogue management in open domain dialogue is a difficult prob- lem, which highlights the challenges of natural language processing. In this thesis, we propose a principal solution to develop dialogue systems in open domains. The key idea of our approach is building dialogue systems which interactively learn from dialogues with users. The interactive learning enables the system to improve and to extend its knowledge base continually. As a part of this...
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New Methods in Statistical Speech Recognition
Klusáček, David ; Hajič, Jan (advisor) ; Psutka, Josef (referee) ; Černocký, Jan (referee)
Title: New Methods in Statistical Speech Recognition Author: David Klusáček Department: Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics in Prague, Malostranské náměstí 25, 118 00 Praha 1. Advisor: Prof. RNDr. Jan Hajič, Dr., Institute of Formal and Applied Linguistics. Abstract: This works aims to identify limits of contemporary speech rec- ognizers and tries to come up with methods that could push back the fron- tiers. After describing the state of the art, the weakest link of the chain has been identified in the acoustic front-end, especially when working in harsh acoustic conditions. NUFIBA front-end, the proposed solution, includes re- verb compensation and speaker/background segmentation as well as contin- uous SNR monitoring which, thru cooperation with acoustic model, hinders from avalanche spreading of recognition errors. Owing to the lack of time, only a phoneme recognizer was finally implemented, although large blocks of originally intended word-based continuous speech recognizer were implemented and tested (such as the MMI-class based language model).
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Tools and Data for Analysis of Spoken Czech and its Prosody
Peterek, Nino ; Hajičová, Eva (advisor) ; Kopeček, Ivan (referee) ; Psutka, Josef (referee)
This work describes our steps towards prosody models of spoken Czech language. After a characterisation and discussion of recent prosody definitions and of area of prosody applications, we present the central point of the work, development of an easy-accessible and user-friendly research environment Dialogy.Org, supporting exploration of Czech prosody and its automatic analysis and modelling. Powered by TCPDF (www.tcpdf.org)
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Speech Recognition of Czech Using Finite-State Machines
Podveský, Petr ; Hajič, Jan (advisor) ; Psutka, Josef (referee) ; Krbec, Pavel (referee)
Speech recognition has become a thriving field with many real-life applications. Voice dialing in cell phones, voice control in embedded devices, speech-driven interactive manuals and many other utilities rely on solid speech recognition software. We believe that research in speech recognition can boost performance of many applications related to the area. The thesis concentrates on automatic large-vocabulary continuous-speech recognition of Czech. Czech differs from English in a few aspects. We focus on these differences and propose new language-depended techniques. Namely rich morphology is investigated and its impact on speech recognition is studied. Out-of-vocabulary (OOV) words are identified as one of the major sources deteriorating recognition performace. New language modeling techniques are proposed to alleviate the problem of OOV words. The proposed language models are tested in speech recognition systems on diverse speech corpora. The obtained results validate the original approach to language modeling. Significant overall speech recognition improvement is observed.
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